Origin of functional de novo genes in humans from "hopeful monsters".

For a long time, it was believed that new genes arise only from modifications of preexisting genes, but the discovery of de novo protein-coding genes that originated from noncoding DNA regions demonstrates the existence of a "motherless" origination process for new genes. However, the features, distributions, expression profiles, and origin modes of these genes in humans seem to support the notion that their origin is not a purely "motherless" process; rather, these genes arise preferentially from genomic regions encoding preexisting precursors with gene-like features. In such a case, the gene loci are typically not brand new. In this short review, we will summarize the definition and features of human de novo genes and clarify their process of origination from ancestral non-coding genomic regions. In addition, we define the favored precursors, or "hopeful monsters," for the origin of de novo genes and present a discussion of the functional significance of these young genes in brain development and tumorigenesis in humans. This article is categorized under: RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution.

Adaptive functions of structural variants in human brain development.

Quantifying the structural variants (SVs) in nonhuman primates could provide a niche to clarify the genetic backgrounds underlying human-specific traits, but such resource is largely lacking. Here, we report an accurate SV map in a population of 562 rhesus macaques, verified by in-house benchmarks of eight macaque genomes with long-read sequencing and another one with genome assembly. This map indicates stronger selective constrains on inversions at regulatory regions, suggesting a strategy for prioritizing them with the most important functions. Accordingly, we identified 75 human-specific inversions and prioritized them. The top-ranked inversions have substantially shaped the human transcriptome, through their dual effects of reconfiguring the ancestral genomic architecture and introducing regional mutation hotspots at the inverted regions. As a proof of concept, we linked APCDD1, located on one of these inversions and down-regulated specifically in humans, to neuronal maturation and cognitive ability. We thus highlight inversions in shaping the human uniqueness in brain development.

6mA-Sniper: Quantifying 6mA sites in eukaryotes at single-nucleotide resolution.

While N6-methyldeoxyadenine (6mA) modification is a fundamental regulation in prokaryotes, its prevalence and functions in eukaryotes are controversial. Here, we report 6mA-Sniper to quantify 6mA sites in eukaryotes at single-nucleotide resolution, and delineate a 6mA profile in Caenorhabditis elegans with 2034 sites. Twenty-six of 39 events with Mnl I restriction endonuclease sites were verified, demonstrating the feasibility of this method. The levels of 6mA sites pinpointed by 6mA-Sniper are generally increased after Pseudomonas aeruginosa infection, but decreased in strains with the removal of METL-9, the dominant 6mA methyltransferase. The enrichment of these sites on specific motif of [GC]GAG, the selective constrains on them, and their coordinated changes with METL-9 levels thus support an active shaping of the 6mA profile by methyltransferase. Moreover, for regions marked by 6mA sites that emerged after infection, an enrichment of up-regulated genes was detected, possibly mediated through a mutual exclusive cross-talk between 6mA and H3K27me3 modification. We thus highlight 6mA regulation as a previously neglected regulator in eukaryotes.

STellaris: a web server for accurate spatial mapping of single cells based on spatial transcriptomics data.

Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, the loss of spatial information hinders its applications in deciphering spatially related features, such as cell-cell interactions in a spatial context. Here, we present STellaris (https://spatial.rhesusbase.com), a web server aimed to rapidly assign spatial information to scRNA-seq data based on their transcriptomic similarity with public spatial transcriptomics (ST) data. STellaris is founded on 101 manually curated ST datasets comprising 823 sections across different organs, developmental stages and pathological states from humans and mice. STellaris accepts raw count matrix and cell type annotation of scRNA-seq data as the input, and maps single cells to spatial locations in the tissue architecture of properly matched ST section. Spatially resolved information for intercellular communications, such as spatial distance and ligand-receptor interactions (LRIs), are further characterized between annotated cell types. Moreover, we also expanded the application of STellaris in spatial annotation of multiple regulatory levels with single-cell multiomics data, using the transcriptome as a bridge. STellaris was applied to several case studies to showcase its utility of adding value to the ever-growing scRNA-seq data from a spatial perspective.